Image Sensor Control for Machine Learning Recognition Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing imaging apparatuses face challenges in determining the cause of insufficient recognition performance, as low sensor performance can be attributed to various characteristics such as resolution, bit length, frame rate, and dynamic range, making it difficult to improve recognition accuracy.
Innovation Solution
The apparatus includes a recognition processing section using a trained machine learning model, a causal analysis section to analyze recognition results, and a control section that adjusts sensor output based on recognition and analysis results, switching between high-resolution low-bit-length and low-resolution high-bit-length image modes to identify and address performance issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor output is increased to improve recognition performance, then recognition accuracy is improved, but data amount and processing load increase
Solution Approach 1:
The sensor output characteristics are dynamically adjusted based on recognition results. When recognition accuracy is insufficient, the system automatically changes sensor parameters (resolution, bit length, frame rate, dynamic range) to capture more informative data, and adjusts spatial arrangement of images for analysis. This dynamic adaptation resolves the contradiction by optimizing data quantity according to actual recognition needs rather than using fixed high-data modes continuously.
Solution Approach 2:
The system changes multiple sensor parameters simultaneously (resolution, bit length, frame rate, dynamic range) to alter the characteristics of sensor output. By adjusting these parameters based on recognition performance feedback, the system can increase or decrease data amount appropriately, resolving the contradiction between recognition accuracy and data quantity.
2Reliability
If sensor characteristics are adjusted to improve recognition performance, then recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The system implements a feedback loop where recognition results are analyzed by a causal analysis section that determines whether sensor performance is the cause of insufficient recognition. Based on this feedback, the control section automatically adjusts sensor output characteristics. This feedback mechanism resolves the contradiction by providing intelligent, automated control that reduces the perceived complexity while achieving improved recognition accuracy.
Solution Approach 2:
The system performs self-diagnosis and self-adjustment. The causal analysis section automatically determines if sensor characteristics are causing recognition failures, and the control section automatically adjusts sensor parameters without external intervention. This self-service capability resolves the contradiction by making the complex adjustment process transparent and automated.
3Measurement precision
If multiple sensor characteristics are monitored for analysis, then cause determination accuracy is improved, but analysis complexity increases
Solution Approach 1:
The system segments the analysis process into distinct functional sections: the causal analysis section receives recognition results and sensor information, separates the analysis of different sensor characteristics (resolution, bit length, frame rate, dynamic range), and determines their individual contributions to recognition performance. This segmentation resolves the contradiction by making the complex multi-parameter analysis structured and manageable.
Data Source
AI summary
Information processing is implemented which analyzes the cause of the result of recognition using a machine learning model.An information processing apparatus includes: a recognition processing section configured to perform object recognition processing on sensor information from a sensor section by use of a trained machine learning model; a causal analysis section configured to analyze the cause of a result of recognition by the recognition processing section on the basis of the sensor information from the sensor section and the result of recognition by the reception processing section; and a control section configured to control an output of the sensor section. The sensor section is an image sensor. The causal analysis section determines the cause of a drop in characteristic of recognition by the recognition processing section on the basis of low-resolution high-bit-length image data for causal analysis.